Real Time Object Detection using CNN based Single Shot Detector Model
Object Detection has been one of the areas of interest of research community for over years and has made significant advances in its journey so far. There is a tremendous scope in the applications that would benefit with more innovations in the domain of object detection. Rapid growth in the field o...
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| Format: | Article |
| Language: | English |
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University of Tehran
2021-01-01
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| Series: | Journal of Information Technology Management |
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| Online Access: | https://jitm.ut.ac.ir/article_80025_9ec794779a11f66b5747fe3f94ea0f77.pdf |
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| author | Abhinav Juneja Sapna Juneja Aparna Soneja Sourav Jain |
| author_facet | Abhinav Juneja Sapna Juneja Aparna Soneja Sourav Jain |
| author_sort | Abhinav Juneja |
| collection | DOAJ |
| description | Object Detection has been one of the areas of interest of research community for over years and has made significant advances in its journey so far. There is a tremendous scope in the applications that would benefit with more innovations in the domain of object detection. Rapid growth in the field of machine learning has complemented the efforts in this area and in the recent times, research community has contributed a lot in real time object detection. In the current work, authors have implemented real time object detection and have made efforts to improve the accuracy of the detection mechanism. In the current research, we have used ssd_v2_inception_coco model as Single Shot Detection models deliver significantly better results. A dataset of more than 100 raw images is used for training and then xml files are generated using labellimg. Tensor flow records generated are passed through training pipelines using the proposed model. OpenCV captures real-time images and CNN performs convolution operations on images. The real time object detection delivers an accuracy of 92.7%, which is an improvement over some of the existing models already proposed earlier. Model detects hundreds of objects simultaneously. In the proposed model, accuracy of object detection significantly improvises over existing methodologies in practice. There is a substantial dataset to evaluate the accuracy of proposed model. The model may be readily useful for object detection applications including parking lots, human identification, and inventory management. |
| format | Article |
| id | doaj-art-d3f0f166f7124049b5de1484cf3708f6 |
| institution | DOAJ |
| issn | 2008-5893 2423-5059 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj-art-d3f0f166f7124049b5de1484cf3708f62025-08-20T03:09:49ZengUniversity of TehranJournal of Information Technology Management2008-58932423-50592021-01-01131628010.22059/jitm.2021.8002580025Real Time Object Detection using CNN based Single Shot Detector ModelAbhinav Juneja0Sapna Juneja1Aparna Soneja2Sourav Jain3Professor, Department of IT, KIET Group of Institutions, Delhi-NCR Ghaziabad, Uttar Pradesh, India.Professor, Department of CSE, IITM Group of Institutions, Sonipat, Haryana, India.Student, Department of CSE, BMIET, Sonipat, Haryana, India.Student, Department of CSE, BMIET, Sonipat, Haryana, India.Object Detection has been one of the areas of interest of research community for over years and has made significant advances in its journey so far. There is a tremendous scope in the applications that would benefit with more innovations in the domain of object detection. Rapid growth in the field of machine learning has complemented the efforts in this area and in the recent times, research community has contributed a lot in real time object detection. In the current work, authors have implemented real time object detection and have made efforts to improve the accuracy of the detection mechanism. In the current research, we have used ssd_v2_inception_coco model as Single Shot Detection models deliver significantly better results. A dataset of more than 100 raw images is used for training and then xml files are generated using labellimg. Tensor flow records generated are passed through training pipelines using the proposed model. OpenCV captures real-time images and CNN performs convolution operations on images. The real time object detection delivers an accuracy of 92.7%, which is an improvement over some of the existing models already proposed earlier. Model detects hundreds of objects simultaneously. In the proposed model, accuracy of object detection significantly improvises over existing methodologies in practice. There is a substantial dataset to evaluate the accuracy of proposed model. The model may be readily useful for object detection applications including parking lots, human identification, and inventory management.https://jitm.ut.ac.ir/article_80025_9ec794779a11f66b5747fe3f94ea0f77.pdfobject detectiondeep learningcnnssdtensor flowopencv |
| spellingShingle | Abhinav Juneja Sapna Juneja Aparna Soneja Sourav Jain Real Time Object Detection using CNN based Single Shot Detector Model Journal of Information Technology Management object detection deep learning cnn ssd tensor flow opencv |
| title | Real Time Object Detection using CNN based Single Shot Detector Model |
| title_full | Real Time Object Detection using CNN based Single Shot Detector Model |
| title_fullStr | Real Time Object Detection using CNN based Single Shot Detector Model |
| title_full_unstemmed | Real Time Object Detection using CNN based Single Shot Detector Model |
| title_short | Real Time Object Detection using CNN based Single Shot Detector Model |
| title_sort | real time object detection using cnn based single shot detector model |
| topic | object detection deep learning cnn ssd tensor flow opencv |
| url | https://jitm.ut.ac.ir/article_80025_9ec794779a11f66b5747fe3f94ea0f77.pdf |
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